More than 2600 restaurants and hospitality venues across the UK trust OrderPay's mobile solution that makes it fast, easy, and cheap for their customers to order, pay, and tip. Through smart use of data, this dynamic, London-based technology scale-up is boosting productivity and user experience - not only for its own busy team, but the wider UK hospitality sector.
Modern data stack
In a market where hospitality venues are battling with inflation, soaring energy costs, price hikes, and staffing shortages, OrderPay’s customers want to get answers to detailed data questions about performance and customer behavior to make smart, profitable decisions. OrderPay had recently outgrown its legacy open source BI solution, which required too much manual intervention and custom SQL code instructions. As OrderPay grew to 70 people in three years and amassed huge data volumes during a turbulent pandemic situation, it was proving challenging for its two-person data team to keep up with user and customer demands.
OrderPay’s Data Analytics Manager Elisa Morariu is responsible for everything from data modeling, to ETL, to analysis, reporting and providing actionable insights to the business and its external partners. One of her first tasks after joining the company was to support the evaluation and selection of a new BI system for OrderPay, together with Steve Callery, Chief Data Officer.
As Morariu explains: “We needed to be able to provide live, advanced analytics for our non-technical users, not just better-looking reports and dashboards. Our previous tool was very code-driven, so we were always having to write SQL queries ourselves to interrogate the data, then build that into a dashboard to make that available to whichever stakeholder needed it. About 40% of my time was taken up by ad hoc queries.”
Morariu and Callery carried out several trials and proofs-of-concept, ultimately selecting ThoughtSpot.
OrderPay has rolled out ThoughtSpot across the company, allowing users in Sales, Marketing, Product, Customer Support, and Operations to play their part continuing to scale the business up profitably. New self-service access to analytics means everyone can explore, drill down and analyze data in areas like spend, revenue, tips, performance by date, location and many other variables to make data-driven decisions. OrderPay then sends reports to the venue operators, which provide valuable insights in performance and transaction details.
After only six months, about 80% of all OrderPay’s data is now available on ThoughtSpot. More than 70% of the company’s users log on and interrogate the system often, about 25% of which are already at power user level. So far, the most popular Liveboards are those used for tracking sales and customer performance. By connecting these two data sources in ThoughtSpot, OrderPay is now able to compare expected to actual performance, which, as Morariu explains, helps the company to support underperforming venue operators:
“There's actually, historically, not been a lot of data capture and analysis like this in our market. With ThoughtSpot, OrderPay is now a pioneer in providing it.” stresses Morariu.
For OrderPay to stay agile and resilient in a very dynamic and turbulent environment, it needed to build and maintain a modern data stack that was future-proof and could deliver data answers at speed. OrderPay’s stack consists of ThoughtSpot, Fivetran for ETL, dbt™ for data transformation, and a Snowflake-based data cloud.
OrderPay’s data team measures success by how relevant colleagues and customers find the analytics, as well as how much extra custom analysis they also require. And on this basis - with a 70% adoption rate - ThoughtSpot is more than paying for itself already.
It’s not just adoption that’s encouraging, but the growth in usage and data literacy that ThoughtSpot is helping drive. Before ThoughtSpot, people had many data questions but didn't want to overload OrderPay’s busy two-person data team. Now users are asking questions they’ve never felt liberated to ask before and freely interrogating and exploring data.
Crucially, with far fewer individual requests to deal with, Morariu can invest more time on modeling data and bringing it onto ThoughtSpot so that the entire company can benefit.
Next, Morariu will focus on further developing OrderPay’s advanced analytics capabilities, exploring different types of product and customer segmentations, Machine Learning-based product recommendations, and smart targeting. Most importantly, as OrderPay’s self-service analytics continues to evolve, OrderPay’s Data team will win back more time to enable the kind of valuable insights that help customers in the hospitality sector to thrive.